Interpretation - Science topic

As both professor. have mentioned, i recommend you to degas your sample at high temperature. Usually high surface area samples with less mass in sample holder results in uplifting of samples during adsorption desoprtion cycle. Take more sample quantity and degas the sample for higher temperature 350 to 400 based on your sample thermal stability.

Good explanation.

Thank u Sir/ Madam

I would like to get the clarification for BD(3). It represents which type of orbital and which type of transition occurs between BD(3) and BD(3)*?

Thank u Sir/ Madam

Good afternoon Subek Sharma, as a developer of deep learning models in collaboration with clinical pathologists, I understand the challenges and possibilities that these models present in medical research. My focus is on balancing accuracy and transparency to ensure that these models are reliable and effective support tools in medical decision-making.

The key to achieving both precision and transparency in deep learning for medical research lies in the synergy between technology and human experience. The deep learning models we develop are designed to identify patterns, characteristics, and sequences that may be difficult for the human eye to discern. This does not imply replacing the physician's judgment, but rather enriching it with deep and detailed insights that can only be discovered through the data processing capabilities of these tools.

Transparency in these models is crucial for generating trust among medical professionals. We are aware that any decision-support tool must be transparent enough for physicians to understand the logic behind the model's recommendations. This involves a continuous effort to develop models whose internal logic is accessible and understandable to health professionals.

In our work, we strive to balance the sophistication of the model with its interpretability. We understand that excessive simplification can compromise the model's ability to capture the complexity in medical data. However, we also recognize that an overly complex model can be an incomprehensible black box for end users. Therefore, our approach focuses on developing models that maintain a high level of accuracy while ensuring that physicians can understand and trust the provided results.

Looking towards the future, we see a scenario where artificial intelligence will not only be a data interpretation tool but also a means for continuous patient monitoring and support. In this landscape, the final decision will always rest with the expert physician, but it will be informed and supported by the deep analysis and perspective that artificial intelligence can provide.

Chinedu Onyeke You can use "Gwyddion" software to analyze AFM data.

Steps:

1. You can drag or import the raw data to open your data

2. (Optional) Use some corrections, if necessary.

3. 'CLICK' on "statistics" to get most of the data required.

If you need more post-process analysis, you can download the user manual.

The knee-jerk reaction of switching to non-parametric tests when the assumption of normal distribution for the variable of interest is obviously unfitting is a common, old-fashioned, and a statistically rather illiterate reaction. It would be more appropriate to go for a better understanding of the data-generating process and to find a more appropriate statistical model allowing to test the relevant hypothesis. You wrote:

"non-parametric tests are usually performed to test the relevant hypotheses"

This may be the intention of the researcher, but this is not what is usually provided by the non-parametric tests. Most of the non-parametric procedures are based on the analysis of ranks, what translates to a comparison of distributional shapes, at best with a relatively high sensitivity to detect location shifts, besides detecting other differences. It seems to be largely ignored that tests like the Wilcoxon-Mann-Whitney U-test is testing the hypothesis of stochastic equivalence, which implies a location shift only under strict additional assumptions which are very clearly unreasonably in almost all practical situations (namely that the distributions are identical in all groups except for the location, so they must all have the same variance and the same kurtosis and all higher moments must also be identical).

Thus, applying an U-test instead of a t-test means to change the hypothesis being tested, demonstrating stochastic non-equivalence may just not be useful for the problem at hand, where typically a conclusion should be drawn about which of the populations has the higher (or lower) expected value.

This is different for non-parametric tests based on a resampling/bootstrapping procedure to infer the sampling distribution under the appropriate null hypothesis. These procedures are rarely used and they have only very little power for small sample sizes. They are also more prone to project any bias in the sample selection affecting other characteristics but the mean into the result.

So just to answer the question in your title: yes, unfortunately!

I'm a firm believer in VISUALIZATION which is why i use R (and if i'm impatient, Excel) for all quantitative - and often for qualitative - analysis. if your study isn't viz-rich, it needs to be!

Dear sir,

I didn't do the experiment myself but sent my protein and analytes for the service. It was failed to determine the kinetics because of the NSB, and the service was ended. I couldn't contact them to ask, and it is quite difficult for me in the first step to understand this method by reading on my own. Now I am trying to understand the result report with only the sensorgrams and figure out what is wrong. There is one beginning step I doubt it will affect the result is that my ligand pI is estimated 4.74 which is too close to their used dilution buffer pH for immobilization (SA, pH5.0) for amine coupling. My protein is truly turned turbid in that buffer.

Otherwise, I am reading to understand the NSB troubleshooting, which BSA using and adding Tween-20 didn't help in their re-run. I was confused because my analyte pI>9, the running buffer pH~7, perhaps caused NSB with the sensor or the protein was not in the right conformation for correct binding.

Anyway, please forgive me for my unclear and annoying question (I will delete the post then). There are still too many basic terms I need to understand it better before asking.

Thank you so much for your time and effort to help me.

It is a decision procedure that assumes too much about measurement and distribution of scores, and is vulnerable to systematic error if the null hypothesis is one of 'no difference' quite a small presence of systematic error will allow its rejection. Only the social 'sciences' use such a loose and vulnerable decision procedure nowadays. Most statisticians recommend at least a confidence interval approach. as I said, see https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5017929/

…

My guess is that it's the increments in the artificial colour scale

CANONICAL CORRESPONDENCE ANALYSIS (CCA AND PARTIAL CCA)

Canonical correspondence analysis investigates the links between a contingency table and a set of variables. Run CCA in Excel using the XLSTAT software.

Put this in google and read this there.

Dear Carl, I'm very sorry, but I don't have any scales or manuals. But I'm sure that someone from the field will eventually come along, read this post and help you out!

Hi Ahmed,

Assuming you have a multichannel image which consists of an image of your bands of interest and an image of your total protein stain you can perform total protein normalisation easily and quickly using our Phoretix 1D software which you can get a free trial of here: https://totallab.com/products/phoretix-1d-free-trial/

We don't have a video demo of how to do this on our YouTube channel yet however the steps would be:

Automatically identify your bands > select normalisation > select total lane volume setting > select reference channel as your total protein stain channel

If you don't have that image, you can combine two images to produce a multichannel image within the software to perform your comparison.

Best wishes,

Steven

Did you get the answer to this question?

I think that this study can be interesting for you

Hello Fisha Mehabaw Alemayoh. Readers will be better able to help you if you report the two ORs with their 95% CIs.

Meanwhile, are you aware of the distinction between positive and negative confounding?

In some disciplines, negative confounding is known as suppression.

HTH.

Yes, the larger sample size in the combined sample could be the source of such a result. In particular, in a regression where the effect in each of the smaller subsamples is "borderline" (i.e., approaching significance), the same effect size could be significant when evaluated with a larger N, due to the smaller standard error associated with that larger N.

Rock physics modeling of reservoir changes is required before the firs 4D seismic acquisition using the available well log data. This will indicate if there is a 4D signature for the expected reservoir changes.

The methods startinf from the Gassman theory are mostly applicable to the sandstone reservoir. Care has to be taken when the carbonate reservoirs are considered. See, for example, Øystein et al. (2005 SEG, Adam, Batzle and Brevik (2006), Vanorio, Scotellaro and Mavko (2007) -SEG, Sain et al. (2008) SEG, and Jiang et al., (2013) SEG.

Hello George Benneh Mensah ,You're absolutely right that AI faces significant limitations when it comes to understanding and answering questions related to complex human emotions, cultural understanding, and subjective judgments. These limitations stem from several key challenges within AI algorithms:

1- Lack of Emotional Intelligence: AI lacks genuine emotional intelligence. While it can analyze and generate text based on patterns it has learned, it does not truly understand or experience emotions. Recognizing and appropriately responding to human emotions, which often involve non-verbal cues and context, is challenging for AI.

2- Cultural Understanding: AI models are trained on vast datasets, but they may not have a deep understanding of the cultural nuances and sensitivities that exist in various parts of the world. Cultural context greatly influences how people perceive and express emotions, making it difficult for AI to navigate these differences.

3- Subjectivity and Ambiguity: Emotions and cultural norms are highly subjective and context-dependent. AI struggles to interpret the nuances of subjective experiences and may provide answers that are overly simplistic or biased based on its training data.

4- Lack of Intuition: Intuition often relies on tacit knowledge and a deep understanding of context, which is challenging for AI to acquire. Human intuition is developed through years of experience, whereas AI models do not have personal experiences to draw upon.

5- Ethical and Moral Considerations: AI systems are not equipped to handle the ethical and moral aspects of emotionally and culturally sensitive questions. Decisions and responses related to these topics require careful consideration of values, which AI lacks.

6- Data Limitations: AI models rely on data to learn patterns, and if the training data does not adequately represent the full spectrum of human emotions and cultural diversity, the model's performance will be limited in these areas.

7- Lack of Common Sense Reasoning: AI often lacks common-sense reasoning, which is crucial for understanding and responding to emotionally and culturally nuanced questions. Common sense involves the ability to fill in gaps in information and make logical inferences, something AI models struggle with.

8- Overreliance on Training Data: AI models can inadvertently learn and perpetuate biases present in their training data, which can lead to skewed or harmful responses to emotionally and culturally based questions.

To address these limitations, ongoing research is focused on improving AI models in areas like emotion recognition, cultural understanding, and ethical considerations. Developing AI with genuine emotional intelligence and a deeper understanding of cultural context remains a complex and evolving challenge. It's also important to consider that while AI can assist in some aspects of these complex questions, human expertise and judgment remain essential, particularly in domains where emotions and cultural nuances are central.

BRs

Artificial intelligence (AI) has become an integral part of our lives, revolutionizing various industries. However, its implications in the legal system raise concerns regarding transparency, interpretability, and the right to a fair trial. While AI can enhance efficiency and accuracy in legal processes, it is crucial to address these issues to ensure justice is served.

Transparency is a fundamental principle of the legal system that ensures accountability and trust. With AI algorithms making decisions, there is a lack of transparency as they often operate as black boxes. This means that the reasoning behind their decisions remains hidden from both lawyers and defendants. Without understanding how AI reaches its conclusions, it becomes challenging to challenge or appeal its verdicts. This lack of transparency undermines the principles of justice and due process.

Interpretability is another critical concern when it comes to AI in the legal system. Legal decisions often require complex reasoning based on statutes, case law, and precedents. However, AI algorithms may struggle with interpreting nuanced legal concepts or adapting to changing circumstances. The inability to understand how AI arrives at its conclusions raises questions about its reliability and accuracy in delivering fair judgments.

Furthermore, the right to a fair trial is at stake with the use of AI in the legal system. Defendants have the right to know how evidence against them was obtained and evaluated. If AI algorithms are used for tasks such as predicting recidivism rates or determining sentencing guidelines without proper oversight or interpretability, it can lead to biased outcomes that disproportionately affect certain groups or individuals.

To address these concerns, policymakers must establish guidelines for ensuring transparency and interpretability in AI systems used within the legal system. These guidelines should include requirements for explainable decision-making processes so that lawyers can effectively challenge algorithmic verdicts if necessary.

In conclusion, while AI has great potential in enhancing efficiency within the legal system, its implications must be carefully considered regarding transparency, interpretability, and ensuring a fair trial for all individuals. By addressing these issues, we can harness the benefits of AI while upholding the principles of justice.

Dear Ali Mameri ,

before starting any interpretation let us have a look at what you are really comparing...

a) You have a reference sample, which not has been exposed to magnetic field. It's black coloured XRD pattern exhibits quite large peaks.

b) You have a sample of same material, but exposed to magnetic field. It's red coloured XRD pattern exhibits quite low peaks relative to the background. The background on both cases is very similar.

My question:

i) are these two samples two different individual ones from the same batch,

or

ii) has the individual reference sample after taking the black XRD pattern being exposed to the magnetic field and then put in the XRD device again for taking the red pattern...

Dear Alexei,

I thought highly of it too.

It had been fundamental in my first degree thesis 89-91, focused on the connections between the undecidability results, Herbrand's theorem, Prolog and theorem proving. In 2022, following that workshop on refutability, I picked up Chang - Lee again, and while searching online to understand if it was an outdated text, I found this ferocious review by C. G. Morgan from 1974, shortly after the book's release. I couldn't help but reflect on how logical investigations proceed as separate interpretive organisms, how they overlap without integrating. It also seemed to me that Morgan's criticism was insignificant, if not wrong, compared to Chang Lee's text.

Now a lot of time passed.

However, I'm glad of your reply.

Paola

Observer dependence is in the

theory rather than experiment.

Following ITS rDNA sequencing, phylogenetic analysis helps place fungal species within an evolutionary framework, offering insights into their evolutionary connections, origins, and diversity. By examining the tree, researchers may learn how different fungi are connected and develop a more thorough understanding of the structure and development of the fungal community.

Satyendra Singh please share the command for intra Hydrogen bond calculation within a protein model. And also give what option we need to choose for intramolecular h bond

The choice is not between SPSS and NVivo. SPSS, by the American company IBM, is for quantitative analysis.

NVivo, Atlas, and Delve are a few of many possibilities. Delve is by far the cheapest, and depending on what you're doing, the other two can be pricey. You could read the following to get started: https://guides.library.jhu.edu/QDAS

Try the following website for some free possibilities: https://sheridancollege.libguides.com/c.php?g=704010&p=5179994

Another common method: Microsoft Excel and/or Google Spreadsheets. There are articles on it.

Suggestion: Try a FREE trial before purchasing. You might be very turned off. Also, some have a much steeper learning curve than others.

It is so clear that one variable is highly correlated with the other variable when it's values between -100 & 100, more points are around zero!!. Regards.

Andrew,

Are you working on 3D Data?

Secondly have you prepared fault polygons in 2D window for onward proceeding to the maps

The same, just that there is no "*when all other IVs are held constant*".

It's simply the (expected) change in Y per unit change of X. There are no other variables involved that would need to be "held constant".

Himanshu Tiwari

Please list these studies so we can comment on them.

No till agriculture could preserve carbon and water in the soil so helping to mitigate anthropogenic global warming.

These are very helpful summaries of Peirce's

Doctrines. Thanks.

In order to interpret a QSAR models you need some clarity on what the underlying chemical processes you are trying to study work. Can you give us more context/info? What type of molecules are you working with? What known reactivity do they have?

Hugo Assunção - a really good answer however, while "Standardized coefficients are helpful when comparing the relative importance of different predictors, especially when predictors are on different scales." is the common text book answer, I don't think this holds water.

Why would using the sample SD put variables on a common scale? You'd have to assume that the variables are measured with equal reliability, coarseness, without range restriction among other things. (This is rarely plausible). Its also debatable whether a beta statistic is a sensible measure of importance (there's other candidates).

I would argue if you know anything about the domain context the unstandardised coefficients are nearly always more interpretable.

I resonate deeply with the perspective of Claude.AI.

Hello Sarah,

As you are apparently considering your outcome measures as distinct (hence, running 18 univariate comparisons rather than some smaller number of multivariate comparisons), one might argue that the sheer number of tests (across outcomes) is irrelevant.

I'm sure you recognize that, in the basic one-between, one-within anova, the interaction is frequently the most important test. Presence of the interaction implies different trajectories over time between groups, or varying group differences across time points. Detailing such differences is important to understanding what went on. One common approach is to use tests of simple effects.

At any rate, the guiding point should be the specific research question/s you are trying to address in your study.

Good luck with your work.

It is difficult to find papers and reviews that justify the threshold of criteria to assess models there are some rules of thumb with range values, for example, for accuracy 50 % is not good it means that your values were predicted randomly, more the percentage is close to 100% better the accuracy is however around 100% meaning that your model overfits, for RMSE MSE lower the value is better your model is because it is translated that your model doesn't have errors. Each model is different according to the included variables. You can run many models changing some parameters and comparing the values between them acknowledging each specificity of the model ensemble learning method will be more accurate than a simple model. You need to select your model according to your research question.

Greetings

The brain works based on associated memories.

You can review an entire day's worth of memories at a glance.

Sometimes in a short dream of REM, you see many events. Or you may sleep for hours and have a very short dream.

Our perception of time is different at different ages.

The passage of time in the brain depends on the release of our neurotransmitters.

The bitter days of time pass more slowly.

Time is actually a creation of our mind, the brain senses time based on environmental changes but does not store it, because our memories are not dependent on the passage of time.

The changes in the wave function depending on each event are recorded in our minds.

Our brain is a quantum computer.

The wave function of every event has an extension in time and space. Time is a real dimension in our brains. Not an imagination...

An important question, I am waiting for the answer, too...

Thank you very much for the exhaustive and detailed response!

thank you for this suggestion

Due to my understanding, it is impossible to continue with MGA without compositional invariance (Step 2). Thus you have to analyze and interpret the group-specific model estimations separately.

Reference

I'm very sorry not to have found the email address of R. G., Cahit Canbay.

There were hundreds of requests for a second opinion of brain and cord MRIs which I couldn't store until today.

Dharti Narwani, I see that you have n = 68 for both "variables" in your output. Is that because you have paired scores? I.e., is there one group of 68 respondents who gave responses to both questions? If so, the analysis needs to account for that pairing. Thank you for clarifying.

Dear Martin Elsner ,

I have sent a similar query to Dave Gardiner in private seven days ago, but he didn't answer until now.

A relevant answer to such a question can only be given if we have more information!

Regards,

Laszlo

Hemoglobinopthies are disorders that involves the hemoglobin protein and this causes an abnormal production and can change the structure of the hemoglobin. According to Biggers (2021), sickle cell anemia, thalassemia, hemoglobin C disease and hemoglobin S-C disease are some examples of hemoglobinopathies. Sickle cell anemia is a type of sickle cell disease where the blood cells forms sickle-shape and die too early. In thalassemia, your body has less hemoglobin than normal and it is caused when the body doesn’t make enough protein which is needed for red blood cells. Hemoglobin C disease is characterized by the target cell in the blood caused by a mutation in the beta-globin chain and the same target cell can be seen in Hemoglobin S-C disease where it is caused by a copy of the Hemoglobin S gene inherited from one parent and a hemoglobin C gene inherited from another.

Source: MediLexicon International. (n.d.). Red Blood Cell Disorders: Types, causes, and symptoms. Medical News Today. https://www.medicalnewstoday.com/articles/red-blood-cell-disorders-types-causes-and-symptoms#definition

I offer you a emerging perspective. I will speak only to dielectric responses (i.e. those modeled using R, C, and the power law term n. Using classical Impedance Spectroscopy, the surface is held at a constant voltage while a small AC potential is applied. The current response is then measured as a function of stepping the AC signal over a broad range of frequency. The data is often reported in terms of the calculated impedance. Data is most often reported in a linear Nyquist plot Z' vs Z" or a Bode Plot log Z', Z", |Z| and Phase angle (Tan theta) verses log frequency. Many models are reported in the literature, however I argue that the instrument only measures current flow through different features of the dielectric film (positive phase angles). If there is only one path, then only a single response (feature) is observed (one semi-circle in the Nyquist plot or one peak in the tangent plot in the Bode plot). the resulting current is indicative of the current path. If there are multiple plots then the current will choose the path based on the complex impedance of the that path, which will change as a function of applied frequency. In terms of the modeling circuit, the behavior is most appropriately modeled as a parallel combination of series RCn legs. Parallel legs because each path is independent of another. Modeled in this way R is related to the ionic current to a particular feature, C is related to inverse thickness of the dielectric and n indicate the population distribution of many micro/nano features contributing to an overall response. The latter n term is a direct result of the fact that EIS is a macro technique (large ascribe area) that is extremely sensitive to micro/nano features. EIS is rather unique in this regard as compared to high magnification spectrographic technique that pick out only the micro/nano features themselves. This perspective, although true as verified by independent measurements or observations, is often clouded by lingering understandings of the technique based only on perspectives of earlier proponent of the technique. The latter is the reason for much confusion and disagreements between interpretation is the literature. In summary, when only positive phase angles are observed, the dielectric response is in terms of relative current flow (as a function of frequency) different features of the film that have dissimilar impedance responses. Thank you for your question. See references: G.A. McRae, M. A. Maguire. Electrochemical Impedance of Anodic Films on Zr-2.5Nb. Journal of The Electrochemical Society 149: B123-B129

G.A. McRae, M.A. Maguire, C.A. Jeffrey, D.A. Guzonas, C.A. Brown. A Comparison of Fractal Dimensions Determined from Atomic Force Microscopy and Impedance Spectroscopy of Anodic Oxides on Zr-2.5Nb. Applied Surface Science 19: 94-105 (2002).

Maguire, M.A. Using EIS/PEDRA to Describe Barrier Oxide Films on Irradiated Zirconium Alloys. MRS Online Proceedings Library 1645, 106 (2014). https://doi.org/10.1557/opl.2014.61

Interpretation of the Avidityof ELISA according to manufactory prochure of the ELISA machine

It is completely my own point of view, and I am afraid to say this. I speculate that there are only two varieties of mathematical operations (be that due to Hermitian operators, arithmetical operators, transformation operators (e.g. reflection, transposition, etc.), action/abstract algebra, set algebra, or anything whatsoever). These are additive or integrative (uneven number of reflections) and subtractive or differentiative/derivative (even number of reflections). What is the Hermitian operator doing in the Minkowski space? Is it capable of doing something else? This is a case of introduction of a nearly-Euclidean quantity into the expanse of 4 dimensions, which can be innovative enough to theorize patterns of 'coming from' and 'going to' between 'general levels of perception (Newtonian/classical mechanics)' and 'quantum levels of events (arising out of Heisenberg's works).' In my humble opinion, your question is something that is inventive/pioneering in nature. If you please, you can see my work that I feel little contextual in this milieu.

Here are some widely recognized sleep quality questionnaires:

Pittsburgh Sleep Quality Index (PSQI): The PSQI is one of the most commonly used questionnaires for assessing overall sleep quality. It covers various aspects of sleep, such as sleep duration, latency, disturbances, and daytime dysfunction. It provides a comprehensive score of sleep quality.

Insomnia Severity Index (ISI): If your research focuses on insomnia specifically, the ISI is a useful tool for assessing the severity of insomnia symptoms. It covers the nature, severity, and impact of insomnia.

Sleep Disturbance Questionnaire (SDQ): The SDQ is a brief questionnaire used to assess sleep disturbances in clinical populations. It covers various aspects of sleep quality and disturbance.

Medical Outcomes Study Sleep Scale (MOS-Sleep): The MOS-Sleep questionnaire is a comprehensive instrument that assesses multiple dimensions of sleep, including sleep disturbance, sleep adequacy, and daytime somnolence.

Wonderful, Thank you so much

Ma'Mon Abu Hammad Could you advise me on some documentation about this test?

Yes, in the context of panel data analysis, the interpretation of unit root tests would typically be conducted for each cross-section or entity separately. This allows for an assessment of the presence or absence of unit roots within each individual entity in the panel.

If you are analyzing panel data and want to avoid testing each station separately, you can consider using panel unit root tests instead of cross-sectional unit root tests. Panel unit root tests take into account the panel structure of the data and allow for testing the presence of unit roots across multiple entities simultaneously.

There are several panel unit root tests available, such as the Levin-Lin-Chu (LLC) test, the Im-Pesaran-Shin (IPS) test, and the Fisher-type tests. These tests consider both the cross-sectional and time-series dimensions of the panel data and can provide more efficient and robust results compared to conducting individual unit root tests for each cross-section.

When using panel unit root tests, you would typically specify the null hypothesis as the presence of a unit root across all entities in the panel. The interpretation would then involve examining the test statistics and p-values to determine whether the null hypothesis can be rejected, indicating the absence of unit roots (stationarity) in the panel.

It's worth noting that the choice of the appropriate panel unit root test may depend on the specific characteristics of your panel data and the assumptions of the underlying model. It's recommended to consult relevant literature or seek expert advice to select the most suitable panel unit root test for your analysis.

Hi, please find this recent research applying unsupervised machine learning for early plant diseases detection:

Hi,

The principle of non-refoulement is enshrined in various international and regional human rights instruments, including the UDHR, ICCPR, and the African Charter on Human and Peoples' Rights. While generally upheld in many African countries as a fundamental aspect of human dignity and autonomy, its application and interpretation can differ based on jurisdiction and specific circumstances. Unlike the more absolute approach of the ECHR's Article 3, some African states may employ exceptions such as national security or public order, under the ICCPR's principle of 'non-derogation', to limit the right to asylum. Nonetheless, regional and subregional mechanisms exist to safeguard refugee rights and address issues related to forced returns in Africa.

Hope this helps.

Hallo Rukaiyat,

I agree with Alfred and Marcos.

Just do mention that Topspin is toally free of charge for acamedia. Meaning you can install a processing version on your provate caomputer too.

MNova has the advantage that you also can analyze other data like MS in addition to NMR. Like it was already mentioned, it is convenien (especially to users new to NMR), but it is noct for free.

Many Greetings,

Kristof

Shagufta Naz First of all, the most important question you should ask, is: what kind of relationship between x and y do you expect, logically, theoretically? THEN choose a model.

If you can give us more details and dat, we can have a look and maybe we will have ideas.

Could you give us some data please, so we can have a look what's going on?

The relative size of the semi circles in the Nyquist plot are only subjective. What is important is the model used to fit the data and phsycial interpretation of the fitting parameters.

The fact that you have two semi circles in the first quadrant of the Nyquist plot indicates only dielectric responses. I suggest you fit your data to a parallel combination of series RCn elements (parallel Debye Responses). If the fit is successful, then the next step is to physically interpret the parameters.

The R parameter is indicative of the response being observed and is an indication of the ionic resistance to a physical feature of the film. The C parameter is inversely proportional to the dielectric thickness. To calculate this you need the effective area and a relative dielectric constant. Initially, assume that the area is the ascribed area of the occulted cell. The relative dielectric constant can be set so that the thickness is in the expected range. Assuming the area and relative dielectric constant are the same for all measurements, the relative thickness of the dielectric can be compared. Finally, the power law term, n, is indicative of the population distribution of many nano or micro features that contribute to the overall response. This is a direct result of EIS being a 'marco' technique the is extremely sensitive to 'nano' or 'micro' contributions.

The EIS technique is unique in this regard; sorta like analyzing numerous high magnification TEM images to get an overall picture of the film. However, EIS does that in one single scan an ambient conditions.

The real challenge of the technique is then to correlate the physical parameters to in-service behavior. Which features best reflect the desired behavior of your film. Armed with this knowledge you are able to quantifiable the physical features the dictate the behavior of your system.

In conclusion, subjectively comparing size of semi circles is not adequate, you must identify a fit model and interpret the fit parameters, and verify that physical attributes determined from the electrical parameters are indicative of you system. Finally, the values of the physical parameters need to be verified by some sort of independent measurement. Anything less than this breeds confusion and distrust in the EIS technique.

Back to your original question, both subjective observations of the size of the semicircles can be correct, however a physical understanding of your system can help to explain why there can be two correct answers.

Vahid Norouzi Larsari

Hi,

Your selection method is chiefly purposive sampling, as you’ve chosen distinct sixth-grade male students from Rezvanshahr based on set criteria, including their OQPT scores. The subsequent random allocation to groups only took place after this deliberate selection, so the principal approach is purposive.

Hope this help.

Dear Ahmad,

A very important question. A lot of phoneticians argue that the tone of voice rather than the lexical content provides the message the speaker tries to convey.

There are both classic and recent texts that deal with this in detail:

Duncan Jr, S., & Niederehe, G. (1974). On signalling that it's your turn to speak. Journal of experimental social psychology, 10(3), 234-247.

Palmer, F. R. (1970). Prosodic Analysis. Language and Language Learning [Series] Number 25.

Knowles, G. (2014). Patterns of spoken English: An introduction to English phonetics. Routledge.

Beňuš, Š., & Beňuš, Š. (2021). Prosody II: Intonation. Investigating Spoken English: A Practical Guide to Phonetics and Phonology Using Praat, 211-234.

Yenkimaleki, M., van Heuven, V. J., & Moradimokhles, H. (2023). The effect of prosody instruction in developing listening comprehension skills by interpreter trainees: does methodology matter?. Computer Assisted Language Learning, 36(5-6), 968-1004.

I hope this is helpful to you!

Hi Amina,

The SEM/EDS quantification of light elements is problematic. This is because of the large variations in absorption effects in the low energy range, as well as the variations in background calculation due to high absorption edges and high statistical errors. Quantification for light elements is difficult even with using ultrathin window or windowless X-ray detectors because of the severe self-absorption of low energy X-rays, poor detection efficiency, and low fluorescence yield.

Most SEM-EDS systems use beam energy of 20 keV because it is a good compromise between the requirements of obtaining adequate overvoltage and minimizing absorption in the specimen. However, such high beam energy will penetrate deep into the steel substrate and may mask the X-rays of light elements that produce energies below 2 keV. Therefore, if your have been using high accelerating voltage, I suggest you try to use a low voltage (≤10 kV) to reduce the x-rays generated from the steel substrate. EDS measurements of light elements and many 3d transition elements can still be accomplished at accelerating voltage as low as 1.5 kV, see E. D. Boyes, Adv. Mat. 1998, 10, 1277.

On another topic, have you tried using FTIR spectroscopy in analysing the bacterial induced corrosion on the steel samples? FTIR is much simpler technique and is more suitable for studying organic substances. You may find the attached paper useful.

Hope this helps,

Kind regards

And what is your question here?

The effects of multicollinearity/redundancy would be similar as in multiple regression analysis since a mediation model consists of a series of regression equations. For example, you might end up with large standard errors or suppressor effects. See, e.g.,

Maassen, G. H., & Bakker, A. B. (2001). Suppressor variables in path models: Definitions and interpretations. Sociological Methods & Research, 30(2), 241-270.

MacKinnon, D. P., Krull, J. L., & Lockwood, C. M. (2000). Equivalence of the mediation, confounding and suppression effect. Prevention Science, 1, 173-181.

What is waving in the Schrödinger equation and why is it called the “wave” of the Schrödinger equation, especially when its phase is undefined?

It is well known that there is theoretical and experimental evidence for a causal relationship between the phase of the wave function and physical reality.

The Copenhagen interpretation of quantum mechanics, which only gives physical meaning to the magnitude of the wave function, cannot be considered complete on this basis.

* A new dynamic-statistical interpretation of quantum mechanics is needed [1,2].

Believe it or not, attaching a well-defined phase to the amplitude of the SE wave would no longer complicate it but on the contrary would make it more understandable and its solution more accessible.

However, we assume that defining a phase at the amplitude of SE can be done via two different approaches:

i-reform the Bohr/Copenhagen interpretation of the Schrödinger equation.

ii-Apply the complex transition matrix Q to find the statistical numerical solution of SE.

To be continued.

1-Ivan Georgiev Koprinkov, Phase Causation of the Wave Function or Can the Copenhagen Interpretation of Quantum Mechanics Be Considered Complete? Journal of Modern Physics Vol.7 No.4, February 2016.

2-I.Abbas,Numerical statistical resolution of the Schrödinger wave equation, Researchgate.

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